The increasing availability of medical imaging data offers unprecedented opportunities for advancing artificial intelligence (AI)-driven healthcare. However, strict data protection regulations in the European Union (EU), especially the General Data Protection Regulation (GDPR), present significant challenges to data sharing and reuse. Synthetic data—artificially generated data that mimic the statistical properties of real data without revealing sensitive information—have emerged as a promising solution to bridge this gap. This perspective-style review examines the role of synthetic medical imaging data within the European Health Data Space (EHDS), a policy initiative aimed at enabling secure access to health data across the EU. While we briefly reference cross-cutting privacy-enhancing technologies and one non-imaging comparator to illuminate shared governance issues, our analysis and conclusions are scoped to imaging applications. We discuss the technical foundations and types of synthetic data, their potential to enhance reproducibility and innovation, and the complex ethical and legal concerns surrounding their use. Emphasising the need for a risk-based regulatory framework, we advocate for synthetic data governance that ensures utility, transparency, and accountability, especially when such data are generated using generative AI models. This work contributes to ongoing debates on how synthetic imaging data can support a privacy-preserving, data-driven healthcare ecosystem in Europe.
Jiang et al. (2025) studied this question.